VARIABLES DE SALUD

#Limpieza

BASE DE DATOS: DENSIDAD DE CAMAS EN LOS CENTROS HOSPITALARIOS (Mayra Vargas)

library(htmltab)
linkCIA_cama = "https://www.cia.gov/library/publications/resources/the-world-factbook/fields/360.html"
linkPath_cama='//*[@id="fieldListing"]'
camas = htmltab(doc = linkCIA_cama,
                which = linkPath_cama)
## No encoding supplied: defaulting to UTF-8.

#LIMPIEZA

library(tidyr)
camas=separate(camas,`Hospital bed density`,into = c("oficial","delete")," ")
## Warning: Expected 2 pieces. Additional pieces discarded in 180 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
camas$delete=NULL
names(camas)=c("Pais","numero de camas por 1000 hab")
camas$`numero de camas por 1000 hab`=as.numeric(camas$`numero de camas por 1000 hab`)
str(camas)
## 'data.frame':    182 obs. of  2 variables:
##  $ Pais                        : chr  "Afghanistan" "Albania" "Algeria" "Andorra" ...
##  $ numero de camas por 1000 hab: num  0.5 2.9 1.9 2.5 3.8 5 4.2 3.8 7.6 4.7 ...

#NA

table(camas$`numero de camas por 1000 hab`,useNA = "always")
## 
##  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9    1  1.1  1.2  1.3  1.4  1.5  1.6 
##    1    2    3    1    3    2    8    6    3    3    4    3    8    5    5    5 
##  1.7  1.8  1.9    2  2.1  2.2  2.3  2.4  2.5  2.6  2.7  2.8  2.9    3  3.1  3.2 
##    3    1    3    3    4    4    2    2    2    4    6    4    5    3    2    1 
##  3.4  3.5  3.6  3.7  3.8    4  4.1  4.2  4.3  4.4  4.5  4.6  4.7  4.8  4.9    5 
##    4    1    2    3    5    2    1    2    2    2    1    1    4    3    1    3 
##  5.2  5.4  5.6  5.7  5.8  5.9  6.2  6.3  6.5  6.7  6.8    7  7.3  7.4  7.6  8.2 
##    1    1    1    1    4    1    1    2    3    1    1    2    1    1    1    2 
##  8.3  8.7  8.8   11 11.5 13.2 13.4 13.8 <NA> 
##    1    1    1    1    1    1    1    1    2

#MERGE PARA QUEDARNOS CON LOS PAÍSES DE EUROPA Y AMÉRICA

library(rio)
lkpais = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpais)
Numerodecamas=merge(camas,Paisesoficial,by.x='Pais',by.y='Pais')

BASE DE DATOS: GASTO ACTUAL EN SALUD (Current Health Expenditure - CHE) (Mayra Vargas)

library(htmltab)
linkCIA_che = "https://www.cia.gov/library/publications/resources/the-world-factbook/fields/409.html"
linkPath_che='//*[@id="fieldListing"]'
che= htmltab(doc = linkCIA_che,
             which = linkPath_che)
## No encoding supplied: defaulting to UTF-8.

#LIMPIEZA

library(tidyr)
che=separate(che,`Current Health Expenditure`,into = c("oficial","delete"),"%")
che$delete=NULL
names(che)=c("Pais","CHE")
che$CHE=as.numeric(che$CHE)
str(che)
## 'data.frame':    193 obs. of  2 variables:
##  $ Pais: chr  "Afghanistan" "Albania" "Algeria" "Andorra" ...
##  $ CHE : num  11.8 6.7 6.4 10.3 2.8 4.5 9.1 10.4 9.2 10.4 ...

#NA

table(che$CHE,useNA = "always")
## 
##  1.2  1.8  2.3  2.4  2.5  2.6  2.8  2.9    3  3.1  3.2  3.3  3.5  3.6  3.7  3.8 
##    1    1    1    1    2    1    2    3    1    2    1    6    3    1    2    4 
##  3.9    4  4.1  4.2  4.4  4.5  4.7  4.8  4.9    5  5.2  5.3  5.5  5.6  5.7  5.8 
##    2    2    2    2    3    6    4    2    2    3    5    4    5    3    1    3 
##  5.9    6  6.1  6.2  6.3  6.4  6.5  6.6  6.7  6.8  6.9    7  7.2  7.3  7.4  7.5 
##    3    2    3    4    2    4    2    2    5    2    4    3    7    2    3    1 
##  7.6  7.7  7.9    8  8.1  8.2  8.3  8.4  8.6  8.7  8.8  8.9    9  9.1  9.2  9.3 
##    3    1    1    2    3    3    2    1    3    1    2    2    3    1    3    2 
##  9.5  9.6  9.8  9.9   10 10.1 10.3 10.4 10.6 10.8 10.9   11 11.2 11.3 11.7 11.8 
##    1    2    1    1    1    2    2    3    1    1    1    2    1    1    1    1 
##   12 12.3 12.4 13.4 16.4 17.1 <NA> 
##    1    1    1    1    1    2    1

#MERGE PARA QUEDARNOS CON LOS PAÍSES DE EUROPA Y AMÉRICA

library(rio)
lkpaises= "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
HealthExpenditure=merge(che,Paisesoficial,by.x='Pais', by.y='Pais')

Base de datos: Asistencia sanitaria universal (Gonzalo Berger)

library(rio)
UHClk='https://github.com/GonzaloBerger/123/raw/master/Data_Extract_From_World_Development_Indicators.xlsx'
datauhc=import(UHClk)
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises)
datauhc[,c(1:2,4:13,15:16)]=NULL
datauhc=datauhc[-c(218:269),]
names(datauhc)=c('Pais','Asistencia sanitaria universal')
datauhc$`Asistencia sanitaria universal`=as.numeric(datauhc$`Asistencia sanitaria universal`)
## Warning: NAs introduced by coercion
datauhc[datauhc$Pais=='Venezuela, RB','Pais']='Venezuela'
datauhc[datauhc$Pais=='Czech Republic','Pais']='Czechia'
datauhc[datauhc$Pais=='Slovak Republic','Pais']='Slovakia'
datauhc[datauhc$Pais=='Russian Federation','Pais']='Russia'

#NA (Gonzalo Berger)

table(datauhc$`Asistencia sanitaria universal`, useNA = 'always')
## 
##   25   28   31   33   37   38   39   40   41   42   43   44   45   46   47   48 
##    1    2    1    1    3    2    4    5    3    3    2    2    4    3    5    4 
##   49   51   52   53   54   55   57   58   59   60   61   62   63   64   65   66 
##    2    1    2    1    1    4    2    2    1    2    5    4    2    3    4    3 
##   68   69   70   71   72   73   74   75   76   77   78   79   80   81   82   83 
##    7    6    5    6    4    6    8    5   11    6    4    5    2    2    4    6 
##   84   86   87   89 <NA> 
##    3    4    4    1   34

##MERGE PARA QUEDARNOS CON LOS PAÍSES DE EUROPA Y AMÉRICA LATINA (Gonzalo Berger)

UHC=merge(datapaises,datauhc)

BASE DE DATOS: OCUPACIÓN HOSPITALARIA (Ivonne Mondoñedo Mora)

library(htmltab)
LinkDoc="https://www.cia.gov/library/publications/resources/the-world-factbook/fields/359.html"
Link_path_doc='//*[@id="fieldListing"]'
Medicos= htmltab (doc = LinkDoc,which = Link_path_doc)
## No encoding supplied: defaulting to UTF-8.

#Limpieza

names(Medicos)
## [1] "Country"            "Physicians density"
names(Medicos)= c ("Pais", "Numero_medicos")
library(tidyr)
Medicos=separate(Medicos,Numero_medicos,into=c("Numero_medicos",'delete'), "\\ ")[,-3]
## Warning: Expected 2 pieces. Additional pieces discarded in 198 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Medicos$Numero_medicos=as.numeric(Medicos$Numero_medicos)
Medicos$Numero_medicos=trimws(Medicos$Numero_medicos,whitespace = "[\\h\\v]")
Medicos$Numero_medicos=as.numeric(Medicos$Numero_medicos)

#Ver los NA’s

table(Medicos$`Numero_medicos`,useNA = "always")
## 
## 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09  0.1 0.11 0.12 0.13 0.14 0.16 0.17 0.18 
##    2    1    2    5    2    3    3    4    1    2    1    1    1    1    3    3 
## 0.19  0.2 0.21 0.22 0.23 0.28 0.31 0.34 0.36 0.37 0.38  0.4 0.41 0.46  0.5 0.52 
##    1    4    1    1    2    1    2    1    2    2    2    1    1    1    1    1 
## 0.53 0.65 0.66 0.72 0.73 0.77 0.78 0.79  0.8 0.81 0.82 0.84 0.86 0.91 0.92 0.93 
##    1    1    1    1    1    1    1    1    1    1    2    1    1    1    1    1 
## 0.95 0.96 0.98 1.01 1.04 1.08 1.13 1.14 1.15 1.19  1.2 1.22 1.23 1.24 1.27 1.28 
##    1    1    1    1    1    2    1    1    1    1    1    1    1    1    2    1 
##  1.3 1.32 1.37 1.42 1.45 1.51 1.56 1.57 1.61  1.7 1.76 1.77 1.79 1.83 1.87 1.88 
##    1    1    1    1    1    1    1    2    1    1    1    1    1    1    1    1 
## 1.89 1.94 1.95 1.96 1.97 2.05 2.08 2.13 2.15 2.16  2.2 2.22 2.25 2.26 2.27 2.31 
##    1    1    1    1    1    1    1    1    1    1    1    2    1    1    1    1 
## 2.33 2.34 2.37 2.39  2.4 2.41 2.46 2.49 2.52 2.54 2.58 2.59 2.61 2.62 2.67 2.72 
##    1    1    2    2    1    2    1    1    1    1    1    1    1    1    1    1 
## 2.76 2.78 2.81 2.87 2.89  2.9 3.01 3.03 3.09 3.13 3.19  3.2 3.22 3.23 3.25 3.32 
##    1    1    2    1    2    1    1    2    1    2    1    1    1    2    1    1 
## 3.33 3.34 3.45 3.47 3.51 3.59 3.67 3.81 3.83 3.96 3.97 3.99 4.01 4.07 4.08 4.09 
##    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
## 4.21 4.24 4.31 4.34 4.46 4.59 4.63 5.05  5.1 5.14  5.4 6.15 6.56 8.19 <NA> 
##    1    1    1    1    1    1    1    1    1    1    1    1    1    1    4

#Merge para quedarnos solo con países de Europa y América

library(rio)
Europa_America3="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer3=import(Europa_America3)
names(EuroAmer3)= c ("Pais")
Doctores_EA =merge(Medicos,EuroAmer3,by.x='Pais', by.y='Pais') 

BASE DE DATOS: che_per_capita (Ivonne Mondoñedo Mora)

library(rio)
Linkche_per_capita="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Current-health-expenditure-per-capita-current-US.xls"
che_per_capita= import(Linkche_per_capita)

#Limpieza

che_per_capita[c(2)]=NULL
names(che_per_capita)= c ("Pais", "Porcentaje_che_per_capita")
library(tidyr)
che_per_capita=separate(che_per_capita,Porcentaje_che_per_capita,into=c("Porcentaje_che_per_capita",'delete'), "\\%")[,-3]
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 231 rows [2, 3,
## 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, ...].
che_per_capita$Porcentaje_che_per_capita=trimws(che_per_capita$Porcentaje_che_per_capita,whitespace = "[\\h\\v]")
che_per_capita[,c(2)]=as.numeric(che_per_capita[,c(2)])

#Ver los NA’s

table(che_per_capita$`Porcentaje_che_per_capita`,useNA = "always")
## 
## 19.4316463470459 21.0711555480957 22.8885726928711 23.2723255157471 
##                1                1                1                1 
## 23.5004329681396 24.1500549316406 24.6708374023438 25.2619533538818 
##                1                1                1                1 
## 29.2616500854492  29.730863571167 30.7664470672607 31.3781051635742 
##                1                1                1                1 
## 32.2593841552734 32.9119987487793 33.7201118469238  33.916576385498 
##                1                1                1                1 
## 36.2823371887207  38.048713684082  38.426441192627  44.403491973877 
##                1                1                1                1 
## 44.5929641723633 44.8095965381493 47.1952864953533 47.9153633117676 
##                1                1                1                1 
## 48.8171310424805 49.2044563293457 49.6357703944185 49.9837226867676 
##                1                1                1                1 
## 52.3594093322754 53.2310967934123 55.0140380859375 55.0745622009202 
##                1                1                1                1 
## 56.5990295410156 57.8998527526855 58.0439567565918  58.760929107666 
##                1                1                1                1 
## 61.4578399658203 62.1246337890625 62.3532791137695 64.4653930414567 
##                1                1                1                2 
## 65.1958396929016  65.896891395763 66.4021835327148 66.7494125366211 
##                1                1                1                1 
## 67.1226501464844 67.6486663818359 67.8118133544922  69.293098449707 
##                1                1                1                1 
## 69.7492446899414 70.3308944702148 73.9249801635742 76.6103210449219 
##                1                1                1                1 
## 78.8228378295898 80.5009177413571  81.409367173405 82.0758666992188 
##                1                1                1                1 
## 83.0623281282646 83.1977081298828 83.7588180080558  94.229377746582 
##                1                1                2                1 
## 96.7993011474609  98.824577331543 101.239868164063 104.552795410156 
##                1                1                1                1 
## 105.666526794434 105.768455505371 110.149620056152  114.45964050293 
##                1                1                1                1 
## 114.971786499023 119.695655822754 129.575958251953 132.900985717773 
##                1                1                1                1 
## 148.784454345703 159.484741210938 161.010864257813 167.589614868164 
##                1                1                1                1 
## 169.716268119907  171.41748046875 177.408889770508 188.414321899414 
##                1                1                1                1 
## 191.185775756836 192.083389282227 193.793045043945 195.935745239258 
##                1                1                1                1 
##  199.00904111287 204.492248535156 210.313705444336 220.274566650391 
##                1                1                1                1 
## 222.015487670898 224.736770629883 230.527282714844 233.065063476563 
##                1                1                1                1 
## 247.035110473633 249.513676366239 250.562225341797 258.481440267046 
##                1                1                1                1 
## 258.494293212891 259.935028076172 262.153071659075 269.496705965721 
##                1                1                2                1 
## 275.809356689453 279.645324707031 280.498809814453 282.491027832031 
##                1                1                1                1 
## 293.053588867188 301.150054931641 307.196044921875 320.593292236328 
##                1                1                1                1 
## 324.586792263897 328.419799804688 332.570922851563 339.327972412109 
##                1                1                1                1 
## 340.661804199219 342.499908447266 344.273677286342 344.289399151357 
##                1                1                1                1 
## 345.789410458349 381.113067626953 384.066070556641 407.635864257813 
##                1                1                1                1 
## 410.185185436188 424.809783935547 433.208587646484 439.594451904297 
##                1                1                1                1 
## 440.825622558594 444.653686523438 447.280639648438 456.482666015625 
##                1                1                1                1 
## 458.960856789522 459.197570800781 459.918042262602 460.068817138672 
##                1                1                1                1 
## 460.473327636719 462.715268773023 465.929321289063 475.479949951172 
##                1                1                1                1 
## 494.677642822266 497.236053466797 499.237548828125 506.216645875853 
##                1                1                1                1 
## 518.029602050781 528.545166015625 555.104736328125 582.671927003548 
##                1                1                1                1 
## 585.873229980469 587.646301269531 599.699768066406 602.038208133858 
##                1                1                1                1 
## 622.176696777344 639.897020371404 642.199157714844 653.926397143921 
##                1                1                1                1 
## 663.715087890625 671.079956165229 671.411499023438 673.859680175781 
##                1                1                1                1 
## 678.874847632542  685.31702184882 719.443481445313 791.656677246094 
##                1                1                1                1 
## 869.077758789063   902.1396484375 902.658996582031 906.820129394531 
##                1                1                1                1 
##  928.79931640625 930.352355957031 934.008050740366 981.423034667969 
##                1                1                1                1 
## 987.627014160156 1006.93878173828 1061.14674489701 1078.17919921875 
##                1                1                1                1 
## 1093.40551757813  1106.7509765625 1112.30322265625 1124.09167480469 
##                1                1                1                1 
## 1127.18627929688 1183.83618164063 1186.13635253906 1300.48168945313 
##                1                1                1                1 
##   1324.603515625 1357.01745605469 1381.98620605469 1475.91516113281 
##                1                1                1                1 
## 1516.58776855469 1529.07763671875   1591.533203125 1596.36291503906 
##                1                1                1                1 
## 1649.18615722656 1731.69445800781 1771.53552246094 1908.03393554688 
##                1                1                1                1 
## 1920.28186035156 2192.44307690792 2283.07470703125    2506.46484375 
##                1                1                1                1 
## 2585.56396484375 2618.71240234375 2840.13061523438      2932.421875 
##                1                1                1                1 
## 3144.62622070313 3261.42622643507 3361.64477539063 3761.33529352997 
##                1                1                1                1 
## 3858.67431640625 3937.22192382813 4040.78662109375   4168.986328125 
##                1                1                1                1 
## 4205.74267578125 4379.72705078125  4507.3564453125 4675.68324696195 
##                1                1                1                1 
## 4754.94775390625  4911.4404296875 4939.87548828125  4976.8623046875 
##                1                1                1                1 
##  5033.4521484375 5284.11754994531 5331.81787109375 5550.62460893834 
##                1                1                1                1 
## 5782.62841796875  5800.1513671875   5904.583984375  6086.3115234375 
##                1                1                1                1 
##         7936.375 9691.08933947145   9956.259765625  10246.138671875 
##                1                1                1                1 
##             <NA> 
##               33

#Merge para quedarnos solo con países de Europa y América

library(rio)
Europa_America2="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer2=import(Europa_America2)
names(EuroAmer2)= c ("Pais")
che_per_capita_EA =merge(che_per_capita,EuroAmer2,by.x='Pais', by.y='Pais') 

VARIABLES DE GOBERNANZA

Limpieza de las bases de datos

#BASE DE DATOS: Estabilidad y ausencia de violencia + Control de Corrupción (Gonzalo Berger)

library(rio)
lkcorrup='https://github.com/GonzaloBerger/123/raw/master/Data_Extract_From_Worldwide_Governance_control_of_corruption.xlsx'
datacorrup=import(lkcorrup)
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises) 
datacorrup[c(2:4)]=NULL
datacorrup=datacorrup[c(1:99),]
names(datacorrup)=c('Pais','Control de la corrupción')
datacorrup$`Control de la corrupción`=as.numeric(datacorrup$`Control de la corrupción`)
## Warning: NAs introduced by coercion
lkviolence='https://github.com/GonzaloBerger/123/raw/master/Data_Extract_From_Worldwide_Governance_Indicators_political_stability_and_absence_of_violence.xlsx'
dataviolence=import(lkviolence)
dataviolence[c(2:4)]=NULL
dataviolence=dataviolence[c(1:99),]
names(dataviolence)=c('Pais','Estabilidad política y ausencia de violencia/terrorismo')
dataviolence$`Estabilidad política y ausencia de violencia/terrorismo`=as.numeric(dataviolence$`Estabilidad política y ausencia de violencia/terrorismo`)
## Warning: NAs introduced by coercion

NA

table(datacorrup$`Control de la corrupción`, useNA = 'always')
## 
## 4.807693     6.25 8.173077 9.615385     12.5 12.98077 16.34615 18.26923 
##        1        1        1        1        1        1        1        1 
##    18.75 20.67308 21.15385 21.63461 22.11539 24.51923 25.96154 28.84615 
##        1        1        1        1        1        1        1        1 
## 29.32692 29.80769 31.73077 32.21154 32.69231 34.61538 35.09615 35.57692 
##        1        1        1        1        1        1        1        1 
## 36.05769 40.38462 41.82692 42.30769 42.78846    43.75 44.23077 44.71154 
##        1        1        1        1        1        1        1        1 
## 46.63462 48.07692 49.03846       50 50.96154 51.92308 52.40385 54.32692 
##        1        1        1        1        1        1        1        1 
## 55.76923 58.17308 59.13462 59.61538 60.09615 61.05769 62.01923 63.46154 
##        1        1        1        1        1        1        1        1 
## 64.42308 66.34615 66.82692 67.78846 68.26923    68.75 69.23077 69.71154 
##        1        1        1        1        1        1        1        1 
## 70.19231 70.67308 72.59615 73.55769 74.03846 74.51923 76.44231 78.84615 
##        1        1        1        1        1        1        1        1 
## 80.28846 80.76923    81.25 81.73077 82.69231 83.17308 84.13461 86.53846 
##        1        1        1        1        1        1        1        4 
## 87.01923     87.5 87.98077 88.46154 88.94231 89.90385 90.38461 90.86539 
##        1        1        1        1        1        1        1        1 
## 91.34615 93.26923    93.75 94.71154 95.19231 95.67308 96.15385 96.63461 
##        1        1        1        1        1        1        1        1 
## 97.11539 97.59615 98.07692 98.55769      100     <NA> 
##        1        1        1        1        1        3
table(dataviolence$`Estabilidad política y ausencia de violencia/terrorismo`, useNA = 'always')
## 
## 6.190476 9.047619       10 17.61905 18.09524       20 20.95238 23.33333 
##        1        1        1        1        1        1        1        1 
## 24.28572 25.23809 25.71428 26.66667 27.14286 29.04762       30 30.47619 
##        1        1        1        1        1        1        1        1 
## 30.95238 31.90476 32.85714 33.33333 35.71429 37.14286 38.09524 39.52381 
##        1        1        1        1        1        1        1        1 
## 40.95238 42.38095 43.33333 45.23809 45.71429 46.19048 46.66667 47.61905 
##        1        1        1        1        1        1        1        1 
## 48.09524 48.57143 49.04762 49.52381       50 50.95238 51.90476 54.76191 
##        1        1        1        1        1        1        1        1 
## 55.23809 56.19048 57.61905 58.09524 58.57143 59.04762 59.52381 60.47619 
##        1        1        1        1        1        1        1        1 
## 60.95238 61.42857 61.90476 62.38095 62.85714 63.80952  64.7619 65.71429 
##        1        1        1        1        1        1        1        1 
## 66.19048 66.66666 67.61905       70 70.47619 72.38095 72.85714 73.33334 
##        1        1        1        1        1        1        1        1 
## 73.80952 76.19048 77.61905 78.09524 78.57143       80 80.47619 80.95238 
##        1        1        1        1        1        1        1        1 
## 81.42857 81.90476 82.38095 84.28571  84.7619  85.2381 85.71429 86.19048 
##        1        1        1        2        1        1        1        1 
## 87.14286 87.61905 89.04762 89.52381       90 90.47619 90.95238 93.33334 
##        1        1        1        1        1        1        1        1 
## 94.28571  95.2381 95.71429 96.19048 96.66666 97.61905 98.09524 99.52381 
##        1        1        1        1        1        1        1        1 
##      100     <NA> 
##        1        1

Merge

corrupcionyviolencia=merge(datacorrup,dataviolence)
CORRUPVIOLENCIA=merge(datapaises,corrupcionyviolencia) 

Base de datos de la base de datos: voz y Accountability: rango de porcentaje (Voice and Accountability: PErcentile Rank) Mayra Vargas

library(rio)
lXvoandac="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Data_From_Worldwide_Governance_Indicators_Voice%20and%20Accountability%20Percentile%20Rank.xlsx"
VoiceandAcc=import(lXvoandac)

Limpieza

VoiceandAcc[,c(2,3,4)]=NULL
names(VoiceandAcc)=c("Pais","VoiceandAccountability")
VoiceandAcc$VoiceandAccountability=as.numeric(VoiceandAcc$VoiceandAccountability)
## Warning: NAs introduced by coercion
str(VoiceandAcc)
## 'data.frame':    99 obs. of  2 variables:
##  $ Pais                  : chr  "Albania" "Andorra" "Anguilla" "Antigua and Barbuda" ...
##  $ VoiceandAccountability: num  53.2 83.3 NA 69.5 67 ...

NA

table(VoiceandAcc$VoiceandAccountability,useNA = "always")
## 
## 0.9852217  4.926108  6.403941  7.881773  9.852217  10.34483  11.82266  15.76355 
##         1         1         1         1         1         1         1         1 
##  18.71921  19.21182  25.12315  26.60098  31.52709  33.99015  35.46798  37.43842 
##         1         1         1         1         1         1         1         1 
##  39.90148  40.39409   40.8867  44.33498  44.82759   45.3202  45.81281  46.30542 
##         1         1         1         1         1         1         1         1 
##  47.29064  48.27586  49.26109   50.2463  51.23153  52.70936  53.20197  55.17241 
##         1         1         1         1         1         1         1         1 
##  56.15763  56.65025  58.12808  58.62069   59.1133  60.59113  61.08374  61.57635 
##         1         1         1         1         1         1         1         1 
##  64.03941  64.53202  65.51724  66.99507  67.48769  68.47291  68.96552  69.45813 
##         2         1         1         1         1         1         1         1 
##  71.42857  71.92118  73.39902  74.38424  74.87685  75.36946  75.86207  76.35468 
##         1         1         1         1         1         1         1         1 
##  76.84729   77.3399  77.83251  78.32513  79.31035  80.78818  81.28078   81.7734 
##         1         1         1         1         1         1         1         1 
##  82.26601  82.75862  83.25123  84.23645  84.72906  87.68473  88.17734  88.66995 
##         1         1         1         1         1         1         1         1 
##  89.16256  89.65517  91.62562  92.11823  92.61084  93.10345  93.59606  94.08867 
##         1         1         4         1         1         1         1         1 
##  94.58128  95.07389  96.05911  96.55173  97.04433  97.53695  98.02956  98.52217 
##         1         1         1         1         1         1         1         1 
##  99.01478       100      <NA> 
##         1         1         5

Merge

library(rio)
lkpaises = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
VoiceandAccountability = merge(VoiceandAcc,Paisesoficial,by.x='Pais',by.y='Pais')

BASE DE DATOS: Efectivida del gobierno - Ivonne Deliany Mondoñedo Mora

library(rio)
Gobernabilidad="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Data_Extract_From_Worldwide_Governance_Indicators%20(3).xlsx"
Gobernanza=import(Gobernabilidad)

Limpieza

names(Gobernanza)= c ("Pais", "Efectividad_gobierno")
Gobernanza$Efectividad_gobierno=as.numeric(Gobernanza$Efectividad_gobierno)
## Warning: NAs introduced by coercion
Gobernanza$Efectividad_gobierno=trimws(Gobernanza$Efectividad_gobierno,whitespace = "[\\h\\v]")
Gobernanza[Gobernanza$Pais=='Russian','Pais']='Russia'

Ver los NA’s

table(Gobernanza$`Efectividad_gobierno`,useNA = "always")
## 
## 1.442308 12.01923 14.42308 19.23077 23.55769 25.48077 27.88461 28.36539 
##        1        1        1        1        1        1        1        1 
## 28.84615 30.28846 33.65385 34.13462 35.57692 36.05769 36.53846 37.98077 
##        1        1        1        1        1        1        1        1 
## 38.46154 39.42308 39.90385 4.807693 40.38462 40.86538 41.82692 42.78846 
##        1        1        1        1        1        1        1        1 
## 43.26923    43.75 44.23077 44.71154 47.11538 47.59615 49.03846       50 
##        1        1        1        1        1        1        1        1 
## 50.96154 51.44231 51.92308 52.88462 53.84615 54.32692 54.80769 55.76923 
##        1        1        1        1        1        1        1        1 
## 56.73077 57.69231 58.17308 60.57692 61.53846 62.98077 65.86539 67.78846 
##        1        1        1        1        2        1        1        1 
## 68.26923    68.75 69.23077 70.19231 70.67308 71.63461 72.59615 73.07692 
##        1        1        1        1        1        1        1        1 
## 74.03846       75 75.48077 76.44231 76.92308 77.88461 78.36539 79.32692 
##        1        1        1        2        1        1        1        1 
## 79.80769 80.28846 80.76923 81.73077 82.69231 83.17308 83.65385 84.61539 
##        1        1        1        1        1        1        1        1 
## 85.57692 86.53846     87.5 87.98077 88.94231 89.42308 89.90385 90.86539 
##        1        1        1        1        1        1        1        1 
## 91.34615 91.82692 92.30769 93.26923 94.71154 95.19231 95.67308 96.15385 
##        1        1        1        1        1        1        1        1 
## 96.63461 97.11539 97.59615 98.55769 99.03846 99.51923     <NA> 
##        1        1        1        1        1        1        3

Merge para quedarnos solo con países de Europa y América

library(rio)
Europa_America4="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer4=import(Europa_America4)
names(EuroAmer4)= c ("Pais")
Gobernanza_EA=merge(Gobernanza,EuroAmer4,by.x='Pais', by.y='Pais') 

Base de datos de estado de derecho: Rango de Percentil (Rule of Law: Percentil Rank)- Mayra Vargas

library(rio)
lkRofLaw= "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Data_From_Worldwide_Governance_Indicators_Rule%20of%20Law%20Percentile%20Rank.xlsx"
RuleofLaw= import(lkRofLaw)

Limpieza

RuleofLaw[,c(2,3,4)] = NULL
names(RuleofLaw)=c("Pais","RuleofLaw")
RuleofLaw$RuleofLaw=as.numeric(RuleofLaw$RuleofLaw)
## Warning: NAs introduced by coercion

NA

table(RuleofLaw$RuleofLaw,useNA = "always")
## 
##        0     6.25 7.692307 9.615385 12.98077 13.46154 14.90385 15.86539 
##        1        1        1        1        1        1        1        1 
## 16.34615 17.78846 19.23077 19.71154 20.19231 20.67308 24.03846 27.40385 
##        1        1        1        1        1        1        1        1 
## 28.84615 29.32692 32.21154 32.69231 35.09615 35.57692 37.01923 38.46154 
##        1        1        1        1        1        1        1        1 
## 38.94231 39.42308 40.38462 41.82692 42.30769    43.75 44.23077 45.67308 
##        1        1        1        1        1        1        1        1 
## 46.15385 46.63462 48.55769 49.03846 50.48077 51.44231 51.92308 52.40385 
##        1        1        1        1        1        1        1        1 
## 53.36538 57.21154 57.69231 59.13462 61.53846     62.5 62.98077 63.46154 
##        1        1        1        1        1        1        1        1 
## 63.94231 65.38461 65.86539 66.82692 67.30769 69.23077 70.19231 71.15385 
##        1        1        1        1        1        1        1        1 
## 72.11539 72.59615 73.07692 73.55769 74.03846 75.96154 76.44231 78.84615 
##        1        1        1        1        1        1        1        2 
## 79.32692 79.80769 80.28846 81.73077 82.69231 83.65385 84.61539 85.09615 
##        1        1        1        1        1        1        1        1 
## 85.57692 86.53846 87.01923 88.46154 88.94231 89.42308 89.90385 90.86539 
##        1        1        1        1        1        1        1        1 
## 91.34615 91.82692 92.30769 93.26923 94.23077 94.71154 95.67308 96.15385 
##        1        1        1        1        2        1        1        1 
## 96.63461 97.59615 98.55769 99.03846 99.51923      100     <NA> 
##        1        1        1        1        1        1        3

#Merge para quedarnos con los países de Europa y América Latina

lkpaises = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
Imperiodelaley=merge(RuleofLaw,Paisesoficial,by.x = 'Pais',by.y = 'Pais')

Base de dato regulatory quality - Mayra Vargas

library(rio)
lkregulatoryq= "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/Regulatory%20Quality%20-%20Percentile%20Rank.xlsx"
Regqual= import(lkregulatoryq)

Limpieza

Regqual[,c(2,3,4)] = NULL
names(Regqual)=c("Pais","RegulatoryQuality")
Regqual[Regqual$Pais=='Venezuela, RB','Pais']='Venezuela'
Regqual$RegulatoryQuality=as.numeric(Regqual$RegulatoryQuality)
## Warning: NAs introduced by coercion
str(Regqual)
## 'data.frame':    99 obs. of  2 variables:
##  $ Pais             : chr  "Albania" "Andorra" "Anguilla" "Antigua and Barbuda" ...
##  $ RegulatoryQuality: num  63.5 85.1 77.4 66.8 42.3 ...

NA

table(Regqual$RegulatoryQuality,useNA = "always")
## 
## 0.4807692  2.884615      6.25  8.653846  12.01923      12.5  15.86539  16.34615 
##         1         1         1         1         1         1         1         1 
##        25  25.96154  26.44231  28.84615  31.73077  32.21154  34.61538  37.01923 
##         1         1         1         1         1         1         1         1 
##  37.98077  39.90385  40.38462  41.34615  42.30769  44.23077  45.19231  45.67308 
##         1         1         1         1         1         1         1         1 
##  48.55769  50.48077  51.92308  52.40385  53.84615  55.28846     56.25  57.69231 
##         1         1         1         1         1         1         1         1 
##  60.09615  60.57692  61.05769  62.01923      62.5  62.98077  63.46154  63.94231 
##         1         1         1         1         1         1         1         1 
##  64.42308  65.38461  65.86539  66.34615  66.82692  67.30769  68.26923  69.23077 
##         1         1         1         1         1         1         1         1 
##  69.71154  70.19231  71.15385  71.63461  72.11539  72.59615  73.07692  73.55769 
##         1         1         1         1         1         1         1         1 
##        75  75.48077  75.96154  77.40385  77.88461  78.36539  78.84615  79.32692 
##         1         1         1         3         1         1         1         1 
##  80.28846  80.76923     81.25  82.69231  83.17308  83.65385  85.09615  85.57692 
##         1         1         1         1         1         1         2         1 
##  86.05769  87.01923      87.5  88.94231  89.90385  90.38461  90.86539  91.34615 
##         1         1         1         1         1         1         1         1 
##  91.82692  92.30769  92.78846     93.75  94.23077  94.71154  95.19231  95.67308 
##         1         1         1         1         1         1         1         1 
##  96.15385  96.63461  97.11539  97.59615  99.03846      <NA> 
##         1         1         1         1         1         3

MERGE

lkpaises = "https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
Paisesoficial=import(lkpaises)
RegulatoryQuality=merge(Regqual,Paisesoficial,by.x='Pais', by.y='Pais')

#VARIABLES DE ACCESO A AGUA POTABLE Y SANEAMIENTO # Limpieza #Base: Acceso a agua potable (Gonzalo Berger)

library(htmltab)
library(tidyr)
library(stringr)
library(rio)
lkpage='https://www.cia.gov/library/publications/resources/the-world-factbook/fields/361.html'
lkpath='//*[@id="fieldListing"]'
Aguapotable = htmltab(doc = lkpage, which = lkpath)
## No encoding supplied: defaulting to UTF-8.
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises) 
Aguapotable = separate(Aguapotable,'Drinking water source',into=c('Z1','Z2'), 'total')
## Warning: Expected 2 pieces. Additional pieces discarded in 220 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [58].
Aguapotable[,c(2)]=NULL
Aguapotable = separate(Aguapotable,'Z2',into=c('Z1','Z2'),'%')
## Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 5,
## 6, 7, 9, 10, 11, 12, 13, 14, 17, 19, 20, 21, 22, 24, 25, 26, ...].
Aguapotable[,c(3)]=NULL
Aguapotable[,c(2)] = gsub(':','',Aguapotable[,c(2)])
Aguapotable[,c(2)] = trimws(Aguapotable[,c(2)],whitespace = '[\\h\\v]')
Aguapotable$Z1=as.numeric(Aguapotable$Z1)
names(Aguapotable)=c('Pais','% of population with access to improved drinking water')

NA

table(Aguapotable$`% of population with access to improved drinking water`, useNA = 'always')
## 
## 31.7   40 51.1 51.5 54.9 55.2 55.5 55.6 55.7 57.8 57.9 58.2 58.7 62.6 63.1 64.4 
##    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
## 65.4 65.8 67.6   68 68.5 68.9 70.2 71.1 71.6 71.9 73.5 73.8   75 75.6 76.1 76.4 
##    1    1    1    1    2    1    1    1    1    1    1    1    1    1    1    1 
## 76.5 76.9   77 78.2 78.3 78.5   79 79.2 79.9 80.3 80.8   81 81.8 82.1 83.7 85.4 
##    1    1    1    1    1    1    1    1    1    2    1    1    1    1    1    1 
## 86.7   87 87.1 87.3 88.4   89 89.3 89.9 90.1 90.2 90.3 90.8   91 91.1 91.4 91.6 
##    1    1    1    1    1    1    1    1    1    1    1    1    2    1    1    1 
## 91.8   92 92.7 92.8 93.1 93.2 93.4 93.6 93.8   94 94.1 94.2 94.5 94.6 94.7 94.8 
##    1    1    1    2    1    1    1    1    2    1    1    2    2    1    1    2 
## 95.1 95.2 95.3 95.6 95.7   96 96.1 96.2 96.3 96.5 96.6 96.7 96.8 96.9   97 97.1 
##    2    1    1    1    1    1    1    3    1    1    1    3    3    2    1    1 
## 97.2 97.3 97.4 97.5 97.6 97.7 97.8 97.9   98 98.1 98.2 98.3 98.4 98.5 98.6 98.7 
##    1    1    5    2    1    2    1    1    1    1    2    2    1    4    3    1 
## 98.9   99 99.1 99.2 99.4 99.5 99.6 99.7 99.8 99.9  100 <NA> 
##    2    3    1    3    1    1    2    3    1    2   60    1

Merge

Agua=merge(datapaises,Aguapotable)

BASE DE DATOS: Saneamiento - Ivonne Deliany Mondoñedo Mora

library(htmltab)
Linksana="https://www.cia.gov/library/publications/resources/the-world-factbook/fields/398.html"
Link_path_sana='//*[@id="fieldListing"]'
saneamiento= htmltab (doc = Linksana,which = Link_path_sana)
## No encoding supplied: defaulting to UTF-8.

Limpieza

names(saneamiento)= c ("Pais", "Porcentaje_Saneamiento")
library(tidyr)
saneamiento$Porcentaje_Saneamiento=gsub("\\%.*","",saneamiento$Porcentaje_Saneamiento)
saneamiento$Porcentaje_Saneamiento=gsub(".*\\:","",saneamiento$Porcentaje_Saneamiento)
saneamiento$Porcentaje_Saneamiento=as.numeric(saneamiento$Porcentaje_Saneamiento)
saneamiento$Porcentaje_Saneamiento=trimws(saneamiento$Porcentaje_Saneamiento,whitespace = "[\\h\\v]")

Ver los NA’s

table(saneamiento$`Porcentaje_Saneamiento`,useNA = "always")
## 
##  100 16.4   18   20 20.2 22.8 24.7 27.2   28 28.5 31.2 31.3 31.4 32.8 33.5 33.6 
##   21    1    1    1    1    1    1    1    1    2    1    1    1    2    1    1 
## 34.1 35.6 37.3 37.5 37.9 40.8 42.4 43.4 43.6 43.8 43.9 44.5 45.1 47.3 48.3 49.3 
##    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
## 50.4 51.2   52 54.5 55.6   56 56.4 57.5 57.7 58.5 59.8 60.8 61.5 61.8 62.5 62.6 
##    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
## 63.1 65.1 65.4 65.6 66.4   69 69.6 72.3 76.1 76.5   77 77.5 77.9 78.5 79.6 79.7 
##    1    1    1    1    1    1    1    1    1    1    1    1    2    1    1    1 
## 79.9 80.7 81.4 81.6 82.3 82.4 82.5 82.9 83.1 83.5 84.1 84.3 84.5 84.7 85.1 85.2 
##    2    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
## 86.2 86.3 86.4 86.6 86.7 86.8   87 87.3 87.8 87.9   88 88.1 88.4 88.6 89.1 89.8 
##    1    1    1    1    1    1    1    1    1    2    2    2    1    1    2    2 
## 89.9 90.5 90.8 91.4 91.5 91.6   92 92.2 92.5 92.8   93 93.3 93.4 93.5 93.8 93.9 
##    1    1    1    1    1    1    1    1    1    1    2    1    1    1    1    1 
## 94.1 94.4 94.5 95.2 95.5 95.6   96 96.1 96.2 96.4 96.6 96.8   97 97.2 97.3 97.4 
##    1    2    1    2    2    1    1    1    4    1    1    2    1    2    1    2 
## 97.5 97.6 97.7 97.8 97.9   98 98.2 98.3 98.4 98.5 98.6 98.7 98.9 99.1 99.2 99.3 
##    8    2    1    2    1    4    1    1    1    1    2    1    1    3    2    3 
## 99.4 99.5 99.6 99.8 99.9 <NA> 
##    2    2    2    1    1    0

Merge para quedarnos solo con países de Europa y América

library(rio)
Europa_America1="https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls"
EuroAmer1=import(Europa_America1)
names(EuroAmer1)= c ("Pais")
Saneamiento_EA =merge(saneamiento,EuroAmer1,by.x='Pais', by.y='Pais')

Limpieza: Muertes confirmadas por millón de habitantes (Gonzalo Berger)

library(htmltab)
library(rio)
library(stringr)
lkpage='https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory'
lkpath='//*[@id="thetable"]'
lkpage2='https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)'
lkpath2='//*[@id="main"]'
covid19=htmltab(doc=lkpage,which = lkpath)
## Warning: Columns [Location,Ref.] seem to have no data and are removed. Use
## rm_nodata_cols = F to suppress this behavior
poblacion=htmltab(doc=lkpage2,which = lkpath2)
lkpaises='https://github.com/IvonneMM/COVID19grupo2oficial/raw/master/America%20y%20Europa3%20(3)%20(1)%20(1)%20(6).xls'
datapaises=import(lkpaises)
covid19[,c(2,4)]=NULL
poblacion[,c(4,6)]=NULL
names(covid19)[names(covid19)=='Location >> World']= 'Pais'
names(poblacion)[names(poblacion)=='Country/Territory >> World']= 'Pais'
poblacion$Pais=str_split(poblacion$Pais,pattern='\\(',simplify = T)[,1]
poblacion$Pais=trimws(poblacion$Pais,whitespace = "[\\h\\v]")
poblacion$Pais=gsub('Â',"",poblacion$Pais)
poblacion$Pais=trimws(poblacion$Pais,whitespace = "[\\h\\v]")
names(covid19)=c('Pais','Muertes_confirmadas')
covid19$Muertes_confirmadas=gsub(',','',covid19$Muertes_confirmadas)
covid19$Muertes_confirmadas=gsub('No data',NA,covid19$Muertes_confirmadas)
covid19=covid19[-c(229:230),]
covid19[,c(2)]=as.numeric(covid19[,c(2)])
names(poblacion)=c('Pais','continentalregion','subregion','Poblacion_2019')
poblacion$Poblacion_2019=gsub(',',"",poblacion$Poblacion_2019)
poblacion$Poblacion_2019=as.numeric(poblacion$Poblacion_2019)
covid19[covid19$Pais=='Saint Vincent','Pais']='Saint Vincent and the Grenadines'

NA

table(covid19$Muertes_confirmadas, useNA = 'always')
## 
##      0      1      2      3      4      5      6      7      8      9     10 
##     30      9      7      5      3      2      1      3      2      3      4 
##     11     13     14     15     16     17     19     20     22     23     24 
##      3      2      2      4      1      1      2      1      1      1      1 
##     26     27     29     32     35     41     42     43     46     49     50 
##      3      2      1      1      2      1      1      1      2      3      1 
##     52     53     54     56     58     59     60     61     64     67     69 
##      2      1      1      1      2      1      1      1      1      2      2 
##     74     75     80     82     83     87     93    102    103    106    109 
##      1      2      1      1      1      1      1      1      1      1      1 
##    114    116    119    124    125    141    145    147    150    151    157 
##      1      1      1      1      1      1      1      1      1      1      2 
##    161    164    174    182    196    206    212    215    219    255    274 
##      1      1      1      1      1      1      1      1      1      1      1 
##    301    329    339    341    351    353    374    382    387    422    441 
##      1      1      1      1      1      1      1      1      1      1      1 
##    447    448    486    493    512    559    573    596    615    718    738 
##      1      1      1      1      1      1      1      1      1      1      1 
##    746    778    793    879   1006   1160   1210   1272   1312   1372   1421 
##      1      1      1      1      1      1      1      1      1      1      1 
##   1693   1705   1716   1735   1763   1924   2023   2343   2866   2894   3111 
##      1      1      1      1      1      1      1      1      1      1      1 
##   3543   4634   4741   4805   5131   5657   5691   5743   5951   6147   8005 
##      1      1      1      1      1      1      1      1      1      1      1 
##   8935   9224   9457   9840  10105  13963  16766  19021  28445  30265  35141 
##      1      1      1      1      1      1      1      1      1      1      1 
##  35747  46000  46119  92568 155660   <NA> 
##      1      1      1      1      1      1

Merge y estandarización

data=merge(covid19,poblacion)
data$'Decesos por millón de habitantes'=(data$Muertes_confirmadas/data$Poblacion_2019)*10^6
data=data[,-c(2,5)]
dataCovid=merge(data,datapaises)

Merge de las variables de salud (Gonzalo Berger)

camasCHE = merge(Numerodecamas,HealthExpenditure,all.x=T,all.y=T)
UHCcamasCHE = merge(UHC,camasCHE,all.x=T,all.y=T)
DoctoresUHCcamasCHE = merge(Doctores_EA,UHCcamasCHE,all.x=T,all.y=T)
DataSalud = merge (che_per_capita_EA,DoctoresUHCcamasCHE,all.x=T,all.y=T)

MERGE DE VARIABLES DE GOBERNANZA - Ivonne Mondoñedo Mora

Accountability_Corrupcion_Violencia=merge(CORRUPVIOLENCIA,VoiceandAccountability,all.x=T, all.y=T)
Gob_Ley=merge(Gobernanza_EA,Imperiodelaley,all.x=T, all.y=T)
Accountability_Corrupcion_Violencia_Gob_Ley=merge(Accountability_Corrupcion_Violencia,Gob_Ley, all.x=T, all.y=T)
Merge_Gobernanza=merge(Accountability_Corrupcion_Violencia_Gob_Ley,RegulatoryQuality, all.x=T, all.y=T)
Merge_Gobernanza$Efectividad_gobierno=as.numeric(Merge_Gobernanza$Efectividad_gobierno)

Merge de las variables de saneamiento y agua potable - Mayra Vargas

Aguaysaneamiento=merge(Agua,Saneamiento_EA,all.x =T,all.y =T)
Aguaysaneamiento$Porcentaje_Saneamiento=as.numeric(Aguaysaneamiento$Porcentaje_Saneamiento)

MERGE FINAL (Mayra Vargas)

merge1=merge(DataSalud,Merge_Gobernanza,all.x = T,all.y = T)
merge2=merge(Aguaysaneamiento,merge1,all.x = T,all.y = T)
Datacovidof=merge(dataCovid,merge2,all.x = T,all.y = T)
names(Datacovidof)
##  [1] "Pais"                                                   
##  [2] "continentalregion"                                      
##  [3] "subregion"                                              
##  [4] "Decesos por millón de habitantes"                       
##  [5] "% of population with access to improved drinking water" 
##  [6] "Porcentaje_Saneamiento"                                 
##  [7] "Porcentaje_che_per_capita"                              
##  [8] "Numero_medicos"                                         
##  [9] "Asistencia sanitaria universal"                         
## [10] "numero de camas por 1000 hab"                           
## [11] "CHE"                                                    
## [12] "Control de la corrupción"                               
## [13] "Estabilidad política y ausencia de violencia/terrorismo"
## [14] "VoiceandAccountability"                                 
## [15] "Efectividad_gobierno"                                   
## [16] "RuleofLaw"                                              
## [17] "RegulatoryQuality"
names(Datacovidof)=c("Pais","Region","Subregion","Decesos","Agua","Saneamiento","CHE_percapita","Medicos","UHC","Camas","CHE","CC","PV","VA","GE","RL","RQ")

Clusters de salud (Gonzalo Berger)

No jarárquico - Partición

library(cluster)
library(factoextra)
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(dbscan)
library(fpc)
## 
## Attaching package: 'fpc'
## The following object is masked from 'package:dbscan':
## 
##     dbscan
set.seed(777)
row.names(Datacovidof)=Datacovidof$Pais
Datacovidof=na.omit(Datacovidof)
g.distsalud = daisy(Datacovidof[,c(7:11)], metric='gower')
fviz_nbclust(Datacovidof[,c(7:11)], pam,diss=g.distsalud,method = 'gap_stat',k.max = 10,verbose = F)

res.pamSALUD=pam(g.distsalud,2,cluster.only = F)
Datacovidof$clusterPAM=res.pamSALUD$cluster
fviz_cluster(object = list(data=g.distsalud, cluster = Datacovidof$clusterPAM),
             geom = c('text'), 
             ellipse.type = 'convex')

Jerarquización

fviz_nbclust(Datacovidof[,c(4,7:11)], hcut,diss=g.distsalud,method = 'gap_stat',k.max = 10,verbose = F)

Aglomerativo - Agnes

res.agSALUD = hcut(g.distsalud, k = 2,hc_func='agnes',hc_method = 'ward.D')
Datacovidof$clusterAG=res.agSALUD$cluster
fviz_dend(res.agSALUD,k=2, cex = 0.5, horiz = T)

Divisivo - Diana

res.diaSALUD = hcut(g.distsalud, k = 2,hc_func='diana')
Datacovidof$clusterDIV=res.diaSALUD$cluster
fviz_dend(res.diaSALUD, cex = 0.5,horiz = T)

Evaluación gráfica con siluetas

fviz_silhouette(res.pamSALUD)
##   cluster size ave.sil.width
## 1       1   49          0.44
## 2       2   18          0.40

fviz_silhouette(res.agSALUD)
##   cluster size ave.sil.width
## 1       1   46          0.46
## 2       2   21          0.37

fviz_silhouette(res.diaSALUD)
##   cluster size ave.sil.width
## 1       1   46          0.46
## 2       2   21          0.37

Evaluación numérica

poorPAMSALUD=data.frame(res.pamSALUD$silinfo$widths)
poorPAMSALUD$Pais=row.names(poorPAMSALUD)
poorPAMcasesSALUD=poorPAMSALUD[poorPAMSALUD$sil_width<0,'Pais']
poorPAMcasesSALUD
## [1] "Ireland"  "Uruguay"  "Portugal"
length(poorPAMcasesSALUD)
## [1] 3
poorAGNESSALUD=data.frame(res.agSALUD$silinfo$widths)
poorAGNESSALUD$Pais=row.names(poorAGNESSALUD)
poorAGNEScasesSALUD=poorAGNESSALUD[poorAGNESSALUD$sil_width<0,'Pais']
poorAGNEScasesSALUD
## character(0)
length(poorAGNEScasesSALUD)
## [1] 0
poorDIANASALUD=data.frame(res.diaSALUD$silinfo$widths)
poorDIANASALUD$Pais=row.names(poorDIANASALUD)
poorDIANAcasesSALUD=poorDIANASALUD[poorDIANASALUD$sil_width<0,'Pais']
poorDIANAcasesSALUD
## character(0)
length(poorDIANAcasesSALUD)
## [1] 0

Estrategia basada en densiadad

proyeccionSALUD = cmdscale(g.distsalud, k=2,add = T)
Datacovidof$dim1 = proyeccionSALUD$points[,1]
Datacovidof$dim2 = proyeccionSALUD$points[,2]
min(Datacovidof[,c('dim1','dim2')]); max(Datacovidof[,c('dim1','dim2')])
## [1] -0.4521162
## [1] 0.3723024
limites=c(-0.5,0.4)
g.dist.cmdSALUD = daisy(Datacovidof[,c('dim1','dim2')], metric = 'euclidean')
kNNdistplot(g.dist.cmdSALUD, k=5)
abline(h=0.13, lty=2)

db.cmdSALUD = dbscan(g.dist.cmdSALUD, eps=0.13, MinPts=5,method = 'dist')
db.cmdSALUD 
## dbscan Pts=67 MinPts=5 eps=0.13
##         1
## border  3
## seed   64
## total  67

CLUSTERS DE GOBERNANZA - Ivonne Mondoñedo

library(cluster)
library(factoextra)
set.seed(777)
row.names(Datacovidof)=Datacovidof$Pais
Datacovidof=na.omit(Datacovidof)
g.dist_gob= daisy(Datacovidof[,c(12,17)], metric="gower")

##Cluster no jerarquico - Partición

fviz_nbclust(Datacovidof[,c(12,17)], pam,diss=g.dist_gob,method = "gap_stat",k.max = 10,verbose = F)

res.pam_gob=pam(g.dist_gob,2,cluster.only = F)

Datacovidof$clusterPT=res.pam_gob$cluster
fviz_cluster(object = list(data=g.dist_gob, cluster = Datacovidof$clusterPT),
             geom = c("text"), 
             ellipse.type = "convex")

#Clusters jerarquicos

fviz_nbclust(Datacovidof[,c(12,17)], hcut,diss=g.dist_gob,method = "gap_stat",k.max = 10,verbose = F)

#Aglomerativo - Agnes

res.agnes_gob = hcut(g.dist_gob, k = 5,hc_func='agnes',hc_method = "ward.D")
Datacovidof$clustAG=res.agnes_gob$cluster
fviz_dend(res.agnes_gob,k=5, cex = 0.5, horiz = T)

#Divisivo - Diana

res.diana_gob = hcut(g.dist_gob, k = 5,hc_func='diana')
Datacovidof$clustDIV=res.diana_gob$cluster
fviz_dend(res.diana_gob, cex = 0.5,horiz = T)

#Evaluación gráfica con siluetas

fviz_silhouette(res.pam_gob)
##   cluster size ave.sil.width
## 1       1   38          0.48
## 2       2   29          0.73

fviz_silhouette(res.agnes_gob)
##   cluster size ave.sil.width
## 1       1   15          0.36
## 2       2    7          0.49
## 3       3   12          0.54
## 4       4   18          0.72
## 5       5   15          0.27

fviz_silhouette(res.diana_gob)
##   cluster size ave.sil.width
## 1       1   15          0.45
## 2       2   23          0.25
## 3       3   18          0.72
## 4       4   10          0.25
## 5       5    1          0.00

#Evaluacion númerica

poorPAM_Gob=data.frame(res.pam_gob$silinfo$widths)
poorPAM_Gob$Pais=row.names(poorPAM_Gob)

poorPAMcases_gob=poorPAM_Gob[poorPAM_Gob$sil_width<0,'Pais']
# osea:
poorPAMcases_gob
## [1] "Antigua and Barbuda" "Hungary"             "Italy"
# agnes
poorAGNES_gob=data.frame(res.agnes_gob$silinfo$widths)
poorAGNES_gob$Pais=row.names(poorAGNES_gob)
poorAGNEScases_gob=poorAGNES_gob[poorAGNES_gob$sil_width<0,'Pais']
poorAGNEScases_gob
## [1] "Brazil"
#diana:
poorDIANA_gob=data.frame(res.diana_gob$silinfo$widths)
poorDIANA_gob$Pais=row.names(poorDIANA_gob)
poorDIANAcases_gob=poorDIANA_gob[poorDIANA_gob$sil_width<0,'Pais']
poorDIANAcases_gob
## [1] "Romania"  "Greece"   "Grenada"  "Bulgaria"

#Estrategia basada en densidad

proyeccion_gob= cmdscale(g.dist_gob, k=2,add = T)
Datacovidof$dim1 <- proyeccion_gob$points[,1]
Datacovidof$dim2 <- proyeccion_gob$points[,2]
base_gob= ggplot(Datacovidof,aes(x=dim1, y=dim2,label=row.names(Datacovidof))) 
base_gob + geom_text(size=2)

Datacovidof$pam=as.factor(res.pam_gob$clustering)
Datacovidof$agnes=as.factor(res.agnes_gob$cluster)
Datacovidof$diana=as.factor(res.diana_gob$cluster)
min(Datacovidof[,c('dim1','dim2')]); max(Datacovidof[,c('dim1','dim2')])
## [1] -0.7459546
## [1] 0.5616711
limites=c(-0.8,0.6)

base_gob= ggplot(Datacovidof,aes(x=dim1, y=dim2)) + ylim(limites) + xlim(limites) + coord_fixed()
base_gob + geom_point(size=2, aes(color=pam))  + labs(title = "PAM") 

base_gob + geom_point(size=2, aes(color=agnes)) + labs(title = "AGNES")

base_gob + geom_point(size=2, aes(color=diana)) + labs(title = "DIANA")

library(dbscan)
library(cluster)

g.dist.cmd_gob=daisy(Datacovidof[,c('dim1','dim2')], metric = 'euclidean')
kNNdistplot(g.dist.cmd_gob, k=6)
abline(h=0.23, lty=2)

library(fpc)
db.cmd_gob= dbscan(g.dist.cmd_gob, eps=0.23, MinPts=6,method = 'dist')
db.cmd_gob
## dbscan Pts=67 MinPts=6 eps=0.23
##         1
## border  3
## seed   64
## total  67
Datacovidof$dbCMD_gob=as.factor(db.cmd_gob$cluster)
library(ggrepel)
base_gob= ggplot(Datacovidof,aes(x=dim1, y=dim2)) + ylim(limites) + xlim(limites) + coord_fixed()
dbplot_gob= base_gob + geom_point(aes(color=dbCMD_gob)) 
dbplot_gob

LABEL=ifelse(Datacovidof$dbCMD_gob==0,row.names(Merge_Gobernanza),"")
dbplot_gob + geom_text_repel(aes(label=LABEL),
                         size=6, 
                         direction = "y", ylim = 0.45,
                         angle=45,
                         segment.colour = "grey")

Clusters de agua potable y saneamiento (Mayra Vargas)

library(cluster)
library(factoextra)
library(stringr)
library(magrittr)
## 
## Attaching package: 'magrittr'
## The following object is masked from 'package:tidyr':
## 
##     extract
set.seed(777)
row.names(Datacovidof)=Datacovidof$Pais
Datacovidof=na.omit(Datacovidof)
distancia= daisy(Datacovidof[,c(5,6)],metric = "gower")

Cluster no jerárquico - partición

fviz_nbclust(Datacovidof[,c(5,6)], pam, diss = distancia,method = "gap_stat",k.max = 10,verbose = F)

pam.resultado = pam(distancia,1,cluster.only = F)
Datacovidof$clusterAS=pam.resultado$cluster
fviz_cluster(object = list(data=distancia, cluster = Datacovidof$clusterAS),
             geom = c("text"), 
             ellipse.type = "convex")

Clusters jerarquicos

fviz_nbclust(Datacovidof[,c(5,6)], hcut, diss = distancia,method = "gap_stat",k.max = 10,verbose = F)

#Aglomerativo - Agnes

res.agnes<-hcut(distancia, k = 1,hc_func='agnes',hc_method = "ward.D")
Datacovidof$clusterAgnes=res.agnes$cluster
fviz_dend(res.agnes,k=1, cex = 0.5, horiz = T)

#Divisivo - Diana

res.diana <- hcut(distancia, k = 1,hc_func='diana')
Datacovidof$clusterDivisivo=res.diana$cluster
fviz_dend(res.diana, cex = 0.7,horiz = T)

#Estrategia basada en densidad

proyeccionAS = cmdscale(distancia, k=2,add = T)
Datacovidof$dim1 <- proyeccionAS$points[,1]
Datacovidof$dim2 <- proyeccionAS$points[,2]

#limites
min(Datacovidof[,c('dim1','dim2')]); max(Datacovidof[,c('dim1','dim2')])
## [1] -0.3484398
## [1] 0.9852876
limites=c(-0.4,1)
g.dist.cmd_AS = daisy(Datacovidof[,c('dim1','dim2')], metric = 'euclidean')
library(dbscan)
kNNdistplot(g.dist.cmd_AS, k=2)
abline(h=0.06,lty=2)

library(fpc)
db.cmd_AS= dbscan(g.dist.cmd_AS, eps=0.06, MinPts=2,method = 'dist')
db.cmd_AS
## dbscan Pts=67 MinPts=2 eps=0.06
##        0  1 2
## border 2  0 0
## seed   0 62 3
## total  2 62 3
Datacovidof$dbCMD_AS=as.factor(db.cmd_AS$cluster)
library(ggrepel)
base_AS= ggplot(Datacovidof,aes(x=dim1, y=dim2)) + ylim(limites) + xlim(limites) + coord_fixed()
dbplot_AS= base_AS + geom_point(aes(color=dbCMD_AS))

dbplot_AS

dbplot_AS + geom_text_repel(size=3,aes(label=row.names(Datacovidof[,c(5,6)])))

LABEL=ifelse(Datacovidof$dbCMD_AS==0,row.names(Datacovidof[,c(5,6)]),"")
dbplot_AS + geom_text_repel(aes(label=LABEL),
                         size=5, 
                         direction = "y", ylim = 0.45,
                         angle=45,
                         segment.colour = "turquoise")

Analisis Factorial Exploratorio (EFA) - Mayra Vargas

Datacovidof=na.omit(Datacovidof)
COVIDEFA=Datacovidof[,c(5:17)]
library(polycor)
MatrixCovid=polycor::hetcor(COVIDEFA)$correlations

Explorar Correlaciones

Sin evaluar significancia

library(ggcorrplot)
ggcorrplot(MatrixCovid)

Evaluando significancia

ggcorrplot(MatrixCovid,
          p.mat = cor_pmat(MatrixCovid),
          insig = "blank")

KMO

library(psych)
## 
## Attaching package: 'psych'
## The following object is masked from 'package:polycor':
## 
##     polyserial
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
psych::KMO(MatrixCovid)
## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = MatrixCovid)
## Overall MSA =  0.88
## MSA for each item = 
##          Agua   Saneamiento CHE_percapita       Medicos           UHC 
##          0.82          0.81          0.89          0.84          0.92 
##         Camas           CHE            CC            PV            VA 
##          0.80          0.74          0.93          0.90          0.92 
##            GE            RL            RQ 
##          0.92          0.90          0.85

Pruebas

HNula: para matriz identidad

cortest.bartlett(MatrixCovid,n=nrow(COVIDEFA))$p.value>0.05
## [1] FALSE

#Hnula: para matriz singular

library(matrixcalc)
is.singular.matrix(MatrixCovid)
## [1] FALSE

#Redimensionar

fa.parallel(COVIDEFA,fm = 'ML', fa = 'fa')

## Parallel analysis suggests that the number of factors =  2  and the number of components =  NA

Primer resultado

library(GPArotation)
resulefa=fa(COVIDEFA,nfactors = 2,cor = 'mixed',rotate = "varimax",fm="minres")
print(resulefa$loadings)
## 
## Loadings:
##               MR1   MR2  
## Agua          0.350 0.732
## Saneamiento   0.333 0.845
## CHE_percapita 0.831 0.141
## Medicos       0.339 0.554
## UHC           0.649 0.374
## Camas               0.556
## CHE           0.496 0.147
## CC            0.864 0.404
## PV            0.720 0.306
## VA            0.895 0.178
## GE            0.861 0.432
## RL            0.894 0.370
## RQ            0.787 0.334
## 
##                  MR1   MR2
## SS loadings    5.936 2.771
## Proportion Var 0.457 0.213
## Cumulative Var 0.457 0.670

Resultado mejorado

print(resulefa$loadings,cutoff = 0.5)
## 
## Loadings:
##               MR1   MR2  
## Agua                0.732
## Saneamiento         0.845
## CHE_percapita 0.831      
## Medicos             0.554
## UHC           0.649      
## Camas               0.556
## CHE                      
## CC            0.864      
## PV            0.720      
## VA            0.895      
## GE            0.861      
## RL            0.894      
## RQ            0.787      
## 
##                  MR1   MR2
## SS loadings    5.936 2.771
## Proportion Var 0.457 0.213
## Cumulative Var 0.457 0.670

Visualizamos

fa.diagram(resulefa)

Evaluación

¿La Raíz del error cuadrático medio corregida está cerca a cero?

resulefa$crms
## [1] 0.08239574

¿La Raíz del error cuadrático medio de aproximación es menor a 0.05?

resulefa$RMSEA
##      RMSEA      lower      upper confidence 
##  0.1762054  0.1476160  0.2094905  0.9000000

¿El índice de Tucker-Lewis es mayor a 0.9?

resulefa$TLI
## [1] 0.8023441

¿Qué variables aportaron mas a los factores?

sort(resulefa$communality)
##           CHE         Camas       Medicos           UHC            PV 
##     0.2679057     0.3138029     0.4217295     0.5606282     0.6117942 
##          Agua CHE_percapita            RQ   Saneamiento            VA 
##     0.6588169     0.7101397     0.7310945     0.8247676     0.8328148 
##            CC            GE            RL 
##     0.9089611     0.9286548     0.9358971

¿Qué variables contribuyen a mas de un factor?

sort(resulefa$complexity)
##         Camas CHE_percapita            VA           CHE   Saneamiento 
##      1.027863      1.057921      1.078813      1.174052      1.302415 
##            RL            RQ            PV            CC          Agua 
##      1.332106      1.347796      1.349005      1.416642      1.434539 
##            GE           UHC       Medicos 
##      1.474242      1.597804      1.656630

Regresión lineal (Gonzalo Berger)

names(Datacovidof)
##  [1] "Pais"            "Region"          "Subregion"       "Decesos"        
##  [5] "Agua"            "Saneamiento"     "CHE_percapita"   "Medicos"        
##  [9] "UHC"             "Camas"           "CHE"             "CC"             
## [13] "PV"              "VA"              "GE"              "RL"             
## [17] "RQ"              "clusterPAM"      "clusterAG"       "clusterDIV"     
## [21] "dim1"            "dim2"            "clusterPT"       "clustAG"        
## [25] "clustDIV"        "pam"             "agnes"           "diana"          
## [29] "dbCMD_gob"       "clusterAS"       "clusterAgnes"    "clusterDivisivo"
## [33] "dbCMD_AS"
library(stargazer)
## 
## Please cite as:
##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
Modelo1=formula(Decesos~CC)
Modelo2=formula(Decesos~CC+PV)
Modelo3=formula(Decesos~CC+PV+VA)
Modelo4=formula(Decesos~CC+PV+VA+GE)
Modelo5=formula(Decesos~CC+PV+VA+GE+RL)
Modelo6=formula(Decesos~CC+PV+VA+GE+RL+RQ)
reg1cov=lm(Modelo1,data=Datacovidof)
stargazer(reg1cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      79.326           
##                              (59.942)          
##                                                
## CC                            1.520*           
##                               (0.908)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.041           
## Adjusted R2                    0.027           
## Residual Std. Error      198.333 (df = 65)     
## F Statistic             2.806* (df = 1; 65)    
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg2cov=lm(Modelo2,data=Datacovidof)
stargazer(reg2cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                     148.913**         
##                              (61.722)          
##                                                
## CC                           5.065***          
##                               (1.499)          
##                                                
## PV                           -5.062***         
##                               (1.753)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.152           
## Adjusted R2                    0.125           
## Residual Std. Error      188.007 (df = 64)     
## F Statistic            5.729*** (df = 2; 64)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg3cov=lm(Modelo3,data=Datacovidof)
stargazer(reg3cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      76.591           
##                              (65.436)          
##                                                
## CC                             1.998           
##                               (1.863)          
##                                                
## PV                           -5.870***         
##                               (1.709)          
##                                                
## VA                            4.593**          
##                               (1.777)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.233           
## Adjusted R2                    0.197           
## Residual Std. Error      180.180 (df = 63)     
## F Statistic            6.386*** (df = 3; 63)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg4cov=lm(Modelo4,data=Datacovidof)
stargazer(reg4cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      59.779           
##                              (68.770)          
##                                                
## CC                             0.741           
##                               (2.422)          
##                                                
## PV                           -5.788***         
##                               (1.717)          
##                                                
## VA                            4.120**          
##                               (1.873)          
##                                                
## GE                             1.894           
##                               (2.322)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.241           
## Adjusted R2                    0.192           
## Residual Std. Error      180.660 (df = 62)     
## F Statistic            4.930*** (df = 4; 62)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg5cov=lm(Modelo5,data=Datacovidof)
stargazer(reg5cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      65.029           
##                              (73.066)          
##                                                
## CC                             0.510           
##                               (2.645)          
##                                                
## PV                           -5.816***         
##                               (1.735)          
##                                                
## VA                            3.940*           
##                               (2.048)          
##                                                
## GE                             1.619           
##                               (2.636)          
##                                                
## RL                             0.658           
##                               (2.902)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.242           
## Adjusted R2                    0.180           
## Residual Std. Error      182.058 (df = 61)     
## F Statistic            3.894*** (df = 5; 61)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg6cov=lm(Modelo6,data=Datacovidof)
stargazer(reg6cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      64.627           
##                              (76.997)          
##                                                
## CC                             0.525           
##                               (2.805)          
##                                                
## PV                           -5.804***         
##                               (1.865)          
##                                                
## VA                            3.927*           
##                               (2.192)          
##                                                
## GE                             1.587           
##                               (3.194)          
##                                                
## RL                             0.636           
##                               (3.186)          
##                                                
## RQ                             0.047           
##                               (2.597)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.242           
## Adjusted R2                    0.166           
## Residual Std. Error      183.568 (df = 60)     
## F Statistic            3.192*** (df = 6; 60)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

#Análisis de varianza

tanova=anova(reg1cov,reg2cov,reg3cov,reg4cov,reg5cov,reg6cov)
stargazer(tanova,type = 'text',summary = F,title = 'Tabla ANOVA')
## 
## Tabla ANOVA
## ====================================================
##   Res.Df      RSS      Df  Sum of Sq    F    Pr(> F)
## ----------------------------------------------------
## 1   65   2,556,836.000                              
## 2   64   2,262,180.000 1  294,655.800 8.744   0.004 
## 3   63   2,045,276.000 1  216,904.100 6.437   0.014 
## 4   62   2,023,549.000 1  21,726.760  0.645   0.425 
## 5   61   2,021,844.000 1   1,704.829  0.051   0.823 
## 6   60   2,021,834.000 1    10.907    0.0003  0.986 
## ----------------------------------------------------

#Regresion lineal parte 2 - Ivonne Mondoñedo Mora

Modelo7=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua)
Modelo8=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento)
Modelo9=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita)
Modelo10=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos)
Modelo11=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos+UHC)
Modelo12=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos+UHC+Camas)
Modelo13=formula(Decesos~CC+PV+VA+GE+RL+RQ+Agua+Saneamiento+CHE_percapita+Medicos+UHC+Camas+CHE)
reg7cov=lm(Modelo7,data=Datacovidof)
stargazer(reg7cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      638.745          
##                              (658.253)         
##                                                
## CC                             1.026           
##                               (2.867)          
##                                                
## PV                           -5.863***         
##                               (1.870)          
##                                                
## VA                            3.749*           
##                               (2.205)          
##                                                
## GE                             2.055           
##                               (3.244)          
##                                                
## RL                             0.392           
##                               (3.204)          
##                                                
## RQ                             0.187           
##                               (2.607)          
##                                                
## Agua                          -6.285           
##                               (7.156)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.252           
## Adjusted R2                    0.163           
## Residual Std. Error      183.919 (df = 59)     
## F Statistic            2.836** (df = 7; 59)    
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg8cov=lm(Modelo8,data=Datacovidof)
stargazer(reg8cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      780.486          
##                              (793.201)         
##                                                
## CC                             1.065           
##                               (2.892)          
##                                                
## PV                           -5.829***         
##                               (1.887)          
##                                                
## VA                            3.929*           
##                               (2.290)          
##                                                
## GE                             2.081           
##                               (3.270)          
##                                                
## RL                             0.082           
##                               (3.367)          
##                                                
## RQ                             0.160           
##                               (2.628)          
##                                                
## Agua                          -9.139           
##                              (11.346)          
##                                                
## Saneamiento                    1.506           
##                               (4.620)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.253           
## Adjusted R2                    0.150           
## Residual Std. Error      185.328 (df = 58)     
## F Statistic            2.457** (df = 8; 58)    
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg9cov=lm(Modelo9,data=Datacovidof)
stargazer(reg9cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      596.790          
##                              (780.035)         
##                                                
## CC                             0.478           
##                               (2.839)          
##                                                
## PV                           -5.667***         
##                               (1.844)          
##                                                
## VA                            3.753*           
##                               (2.237)          
##                                                
## GE                             0.627           
##                               (3.277)          
##                                                
## RL                            -0.667           
##                               (3.309)          
##                                                
## RQ                             0.518           
##                               (2.573)          
##                                                
## Agua                          -6.587           
##                              (11.153)          
##                                                
## Saneamiento                    1.803           
##                               (4.513)          
##                                                
## CHE_percapita                 0.029*           
##                               (0.015)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.300           
## Adjusted R2                    0.190           
## Residual Std. Error      180.935 (df = 57)     
## F Statistic            2.719** (df = 9; 57)    
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg10cov=lm(Modelo10,data=Datacovidof)
stargazer(reg10cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                      599.203          
##                              (787.424)         
##                                                
## CC                             0.502           
##                               (2.878)          
##                                                
## PV                           -5.650***         
##                               (1.872)          
##                                                
## VA                             3.678           
##                               (2.423)          
##                                                
## GE                             0.725           
##                               (3.500)          
##                                                
## RL                            -0.655           
##                               (3.341)          
##                                                
## RQ                             0.476           
##                               (2.640)          
##                                                
## Agua                          -6.636           
##                              (11.266)          
##                                                
## Saneamiento                    1.868           
##                               (4.615)          
##                                                
## CHE_percapita                 0.029*           
##                               (0.015)          
##                                                
## Medicos                       -1.904           
##                              (22.291)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.300           
## Adjusted R2                    0.176           
## Residual Std. Error      182.531 (df = 56)     
## F Statistic            2.405** (df = 10; 56)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg11cov=lm(Modelo11,data=Datacovidof)
stargazer(reg11cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                     -324.635          
##                              (772.156)         
##                                                
## CC                             0.365           
##                               (2.641)          
##                                                
## PV                           -5.623***         
##                               (1.718)          
##                                                
## VA                             1.676           
##                               (2.301)          
##                                                
## GE                            -0.694           
##                               (3.238)          
##                                                
## RL                             0.594           
##                               (3.088)          
##                                                
## RQ                             1.650           
##                               (2.447)          
##                                                
## Agua                          -3.002           
##                              (10.393)          
##                                                
## Saneamiento                   -2.542           
##                               (4.430)          
##                                                
## CHE_percapita                  0.013           
##                               (0.015)          
##                                                
## Medicos                       -13.429          
##                              (20.734)          
##                                                
## UHC                          14.941***         
##                               (4.404)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.422           
## Adjusted R2                    0.306           
## Residual Std. Error      167.491 (df = 55)     
## F Statistic           3.643*** (df = 11; 55)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg12cov=lm(Modelo12,data=Datacovidof)
stargazer(reg12cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                     -314.188          
##                              (779.538)         
##                                                
## CC                             0.351           
##                               (2.664)          
##                                                
## PV                           -5.565***         
##                               (1.744)          
##                                                
## VA                             1.559           
##                               (2.356)          
##                                                
## GE                            -0.696           
##                               (3.266)          
##                                                
## RL                             0.683           
##                               (3.129)          
##                                                
## RQ                             1.687           
##                               (2.471)          
##                                                
## Agua                          -3.116           
##                              (10.488)          
##                                                
## Saneamiento                   -2.298           
##                               (4.548)          
##                                                
## CHE_percapita                  0.013           
##                               (0.015)          
##                                                
## Medicos                       -11.125          
##                              (22.405)          
##                                                
## UHC                          14.718***         
##                               (4.509)          
##                                                
## Camas                         -3.486           
##                              (12.179)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.422           
## Adjusted R2                    0.294           
## Residual Std. Error      168.907 (df = 54)     
## F Statistic           3.291*** (df = 12; 54)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
reg13cov=lm(Modelo13,data=Datacovidof)
stargazer(reg13cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                     -496.129          
##                              (775.065)         
##                                                
## CC                             0.403           
##                               (2.622)          
##                                                
## PV                           -4.495**          
##                               (1.834)          
##                                                
## VA                             1.505           
##                               (2.319)          
##                                                
## GE                            -0.840           
##                               (3.215)          
##                                                
## RL                            -0.246           
##                               (3.131)          
##                                                
## RQ                             2.753           
##                               (2.516)          
##                                                
## Agua                          -3.472           
##                              (10.325)          
##                                                
## Saneamiento                   -2.277           
##                               (4.476)          
##                                                
## CHE_percapita                 -0.006           
##                               (0.018)          
##                                                
## Medicos                       -22.934          
##                              (23.176)          
##                                                
## UHC                          14.887***         
##                               (4.439)          
##                                                
## Camas                         -1.042           
##                              (12.077)          
##                                                
## CHE                           25.208           
##                              (15.217)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.451           
## Adjusted R2                    0.316           
## Residual Std. Error      166.243 (df = 53)     
## F Statistic           3.347*** (df = 13; 53)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
tanova_covid=anova(reg1cov,reg2cov,reg3cov,reg4cov,reg5cov,reg6cov,reg7cov,reg8cov,reg9cov,reg10cov,reg11cov,reg12cov,reg13cov)
stargazer(tanova_covid,type = 'text',summary = F,title = 'Tabla ANOVA')
## 
## Tabla ANOVA
## =====================================================
##    Res.Df      RSS      Df  Sum of Sq    F    Pr(> F)
## -----------------------------------------------------
## 1    65   2,556,836.000                              
## 2    64   2,262,180.000 1  294,655.800 10.662  0.002 
## 3    63   2,045,276.000 1  216,904.100 7.848   0.007 
## 4    62   2,023,549.000 1  21,726.760  0.786   0.379 
## 5    61   2,021,844.000 1   1,704.829  0.062   0.805 
## 6    60   2,021,834.000 1    10.907    0.0004  0.984 
## 7    59   1,995,743.000 1  26,090.100  0.944   0.336 
## 8    58   1,992,096.000 1   3,647.049  0.132   0.718 
## 9    57   1,866,028.000 1  126,068.100 4.562   0.037 
## 10   56   1,865,785.000 1    242.979   0.009   0.926 
## 11   55   1,542,925.000 1  322,859.900 11.682  0.001 
## 12   54   1,540,589.000 1   2,336.810  0.085   0.772 
## 13   53   1,464,744.000 1  75,844.720  2.744   0.104 
## -----------------------------------------------------

DIAGNOSTICO DE LA REGRESIÓN

#linealidad

plot(reg11cov,1)

#Homocedasticidad

library(lmtest)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
plot(reg11cov,3)

bptest(reg11cov)
## 
##  studentized Breusch-Pagan test
## 
## data:  reg11cov
## BP = 15.239, df = 11, p-value = 0.1718

#Normalidad de residuos

shapiro.test(reg11cov$residuals)
## 
##  Shapiro-Wilk normality test
## 
## data:  reg11cov$residuals
## W = 0.96983, p-value = 0.1028

#multicolinealidad

library(DescTools)
## 
## Attaching package: 'DescTools'
## The following objects are masked from 'package:psych':
## 
##     AUC, ICC, SD
VIF(reg11cov)
##            CC            PV            VA            GE            RL 
##     11.869124      3.669769      7.641722     14.814031     16.714802 
##            RQ          Agua   Saneamiento CHE_percapita       Medicos 
##      8.791741      4.304611      4.976820      2.930856      2.094508 
##           UHC 
##      2.653844

#Valores influyentes

plot(reg11cov, 5)

checkReg11=as.data.frame(influence.measures(reg11cov)$is.inf)
head(checkReg11)
##                     dfb.1_ dfb.CC dfb.PV dfb.VA dfb.GE dfb.RL dfb.RQ dfb.Agua
## Antigua and Barbuda  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE
## Argentina            FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE
## Armenia              FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE
## Austria              FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE
## Azerbaijan           FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE
## Barbados             FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE
##                     dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC dffit cov.r cook.d   hat
## Antigua and Barbuda    FALSE    FALSE    FALSE   FALSE FALSE FALSE  FALSE FALSE
## Argentina              FALSE    FALSE    FALSE   FALSE FALSE FALSE  FALSE FALSE
## Armenia                FALSE    FALSE    FALSE   FALSE FALSE FALSE  FALSE FALSE
## Austria                FALSE    FALSE    FALSE   FALSE FALSE FALSE  FALSE FALSE
## Azerbaijan             FALSE    FALSE    FALSE   FALSE FALSE  TRUE  FALSE FALSE
## Barbados               FALSE    FALSE    FALSE   FALSE FALSE FALSE  FALSE FALSE
checkReg11[checkReg11$cook.d | checkReg11$hat,]
##       dfb.1_ dfb.CC dfb.PV dfb.VA dfb.GE dfb.RL dfb.RQ dfb.Agua dfb.Snmn
## Cuba   FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE    FALSE
## Haiti  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE    FALSE    FALSE
##       dfb.CHE_ dfb.Mdcs dfb.UHC dffit cov.r cook.d  hat
## Cuba     FALSE    FALSE   FALSE FALSE  TRUE  FALSE TRUE
## Haiti    FALSE    FALSE   FALSE FALSE  TRUE  FALSE TRUE
Modelo14=formula(Decesos~PV+Agua+Saneamiento+CHE_percapita+Medicos+UHC)
reg14cov=lm(Modelo14,data=Datacovidof)
stargazer(reg14cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                     -605.278          
##                              (711.667)         
##                                                
## PV                           -4.092***         
##                               (1.242)          
##                                                
## Agua                          -0.692           
##                               (9.726)          
##                                                
## Saneamiento                   -1.107           
##                               (4.085)          
##                                                
## CHE_percapita                 0.027**          
##                               (0.012)          
##                                                
## Medicos                       -23.437          
##                              (17.649)          
##                                                
## UHC                          15.864***         
##                               (4.196)          
##                                                
## -----------------------------------------------
## Observations                    67             
## R2                             0.368           
## Adjusted R2                    0.305           
## Residual Std. Error      167.552 (df = 60)     
## F Statistic            5.835*** (df = 6; 60)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
tanova_covid=anova(reg11cov,reg14cov)
stargazer(tanova_covid,type = 'text',summary = F,title = 'Tabla ANOVA')
## 
## Tabla ANOVA
## ====================================================
##   Res.Df      RSS      Df  Sum of Sq     F   Pr(> F)
## ----------------------------------------------------
## 1   55   1,542,925.000                              
## 2   60   1,684,416.000 -5 -141,490.800 1.009  0.421 
## ----------------------------------------------------
plot(reg14cov,1)

library(lmtest)
plot(reg14cov,3)

bptest(reg14cov)
## 
##  studentized Breusch-Pagan test
## 
## data:  reg14cov
## BP = 11.598, df = 6, p-value = 0.07156
shapiro.test(reg14cov$residuals)
## 
##  Shapiro-Wilk normality test
## 
## data:  reg14cov$residuals
## W = 0.94954, p-value = 0.008624
library(DescTools)
VIF(reg14cov)
##            PV          Agua   Saneamiento CHE_percapita       Medicos 
##      1.916598      3.767320      4.229073      2.099088      1.516429 
##           UHC 
##      2.407096
plot(reg14cov, 5)

checkReg14=as.data.frame(influence.measures(reg14cov)$is.inf)
head(checkReg14)
##                     dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC
## Antigua and Barbuda  FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Argentina            FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Armenia              FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Austria              FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Azerbaijan           FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Barbados             FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
##                     dffit cov.r cook.d   hat
## Antigua and Barbuda FALSE FALSE  FALSE FALSE
## Argentina           FALSE FALSE  FALSE FALSE
## Armenia             FALSE FALSE  FALSE FALSE
## Austria             FALSE FALSE  FALSE FALSE
## Azerbaijan          FALSE FALSE  FALSE FALSE
## Barbados            FALSE FALSE  FALSE FALSE
checkReg14[checkReg14$cook.d | checkReg14$hat,]
##       dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC dffit cov.r
## Cuba   FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE FALSE  TRUE
## Haiti  FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE FALSE  TRUE
##       cook.d  hat
## Cuba   FALSE TRUE
## Haiti  FALSE TRUE
Datacovidof=Datacovidof[-c(19,34),]
Modelo14=formula(Decesos~PV+Agua+Saneamiento+CHE_percapita+Medicos+UHC)
reg14cov=lm(Modelo14,data=Datacovidof)
stargazer(reg14cov,type='text',intercept.bottom = F)
## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               Decesos          
## -----------------------------------------------
## Constant                     -717.025          
##                              (872.136)         
##                                                
## PV                           -4.154***         
##                               (1.274)          
##                                                
## Agua                          -0.291           
##                              (10.266)          
##                                                
## Saneamiento                   -1.037           
##                               (4.440)          
##                                                
## CHE_percapita                 0.024*           
##                               (0.013)          
##                                                
## Medicos                       -16.856          
##                              (21.465)          
##                                                
## UHC                          16.643***         
##                               (4.486)          
##                                                
## -----------------------------------------------
## Observations                    65             
## R2                             0.361           
## Adjusted R2                    0.295           
## Residual Std. Error      169.686 (df = 58)     
## F Statistic            5.466*** (df = 6; 58)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
plot(reg14cov,1)

library(lmtest)
plot(reg14cov,3)

bptest(reg14cov)
## 
##  studentized Breusch-Pagan test
## 
## data:  reg14cov
## BP = 10.332, df = 6, p-value = 0.1113
shapiro.test(reg14cov$residuals)
## 
##  Shapiro-Wilk normality test
## 
## data:  reg14cov$residuals
## W = 0.95215, p-value = 0.01359
library(DescTools)
VIF(reg14cov)
##            PV          Agua   Saneamiento CHE_percapita       Medicos 
##      1.898414      2.194219      2.447167      2.402427      1.619122 
##           UHC 
##      2.164574
plot(reg14cov, 5)

checkReg14=as.data.frame(influence.measures(reg14cov)$is.inf)
head(checkReg14)
##                     dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC
## Antigua and Barbuda  FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Argentina            FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Armenia              FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Austria              FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Azerbaijan           FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
## Barbados             FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE
##                     dffit cov.r cook.d   hat
## Antigua and Barbuda FALSE FALSE  FALSE FALSE
## Argentina           FALSE FALSE  FALSE FALSE
## Armenia             FALSE FALSE  FALSE FALSE
## Austria             FALSE FALSE  FALSE FALSE
## Azerbaijan          FALSE FALSE  FALSE FALSE
## Barbados            FALSE FALSE  FALSE FALSE
checkReg14[checkReg14$cook.d | checkReg14$hat,]
##               dfb.1_ dfb.PV dfb.Agua dfb.Snmn dfb.CHE_ dfb.Mdcs dfb.UHC dffit
## Bolivia        FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE FALSE
## United States  FALSE  FALSE    FALSE    FALSE    FALSE    FALSE   FALSE FALSE
##               cov.r cook.d  hat
## Bolivia        TRUE  FALSE TRUE
## United States  TRUE  FALSE TRUE